Inductive logic programming at 30 [PDF]
Machine Learning, 2021 Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge.
Andrew Cropper +3 more
semanticscholar +7 more sources
Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study [PDF]
BMC Bioinformatics, 2012 Background There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically
A Santos Jose C +4 more
doaj +3 more sources
A History of Probabilistic Inductive Logic Programming [PDF]
Frontiers in Robotics and AI, 2014 The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming.
Fabrizio eRiguzzi +2 more
doaj +5 more sources
Knowledge Discovery in Variant Databases Using Inductive Logic Programming [PDF]
Bioinformatics and Biology Insights, 2013 Understanding the effects of genetic variation on the phenotype of an individual is a major goal of biomedical research, especially for the development of diagnostics and effective therapeutic solutions.
Hoan Nguyen +3 more
doaj +3 more sources
Knowledge discovery for pancreatic cancer using inductive logic programming. [PDF]
IET Syst Biol, 2014 Pancreatic cancer is a devastating disease and predicting the status of the patients becomes an important and urgent issue. The authors explore the applicability of inductive logic programming (ILP) method in the disease and show that the accumulated ...
Qiu Y +6 more
europepmc +2 more sources
Differentiable Inductive Logic Programming for Structured Examples [PDF]
AAAI Conference on Artificial Intelligence, 2021 The differentiable implementation of logic yields a seamless combination of symbolic reasoning and deep neural networks. Recent research, which has developed a differentiable framework to learn logic programs from examples, can even acquire reasonable ...
Hikaru Shindo +2 more
openalex +3 more sources
Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2021 Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data.
Prithviraj Sen +3 more
openalex +3 more sources
Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification [PDF]
Conference on Empirical Methods in Natural Language Processing, 2020 Interpretability of predictive models is becoming increasingly important with growing adoption in the real-world. We present RuleNN, a neural network architecture for learning transparent models for sentence classification.
Prithviraj Sen +6 more
openalex +2 more sources
A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models [PDF]
BMC Bioinformatics, 2011 Background Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions.
Carbone Alessandra +2 more
doaj +2 more sources
Best-effort inductive logic programming via fine-grained cost-based hypothesis generation [PDF]
Machine-mediated learning, 2018 We describe the Inspire system which participated in the first competition on inductive logic programming (ILP). Inspire is based on answer set programming (ASP).
Peter Schüller, Mishal Benz
openalex +3 more sources